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All Journal ELINVO (Electronics, Informatics, and Vocational Education) Journal of Engineering and Applied Technology Journal of Logistics and Supply Chain
Akhsin Nurlayli
Universitas Negeri Yogyakarta
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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Readiness to Implement Smart Logistics from an International Perspective : A Review Aulia Zikri Rahman; Vina Dwiyanti; Akhsin Nurlayli
Journal of Logistics and Supply Chain Vol 1, No 1 (2021): April 2021
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (330.389 KB) | DOI: 10.17509/jlsc.v1i1.32873

Abstract

Modern logistics technology may be the key to breaking the deadlock. Utilizing modern logistics technology to build an efficient logistics platform is an effective way to capture opportunities in today's global competitive environment. However, modern logistics still encounter several challenges. Fortunately, the development of big data and smart technology has driven the development of smart logistics. Building a smart logistics platform is conducive to controlling costs, increasing efficiency, reducing energy consumption, etc. With advances in information technology, the existing modern logistics technology can be enhanced to produce maximum and measurable output. This paper aims to discuss findings about the extent to which artificial intelligence is applied in supporting logistical activities by targeting several previous studies. The following literature study aims to determine the level of usability that has been applied in the implementation stage. In order to obtain data or information, proposers conduct a review of previous studies. This literature study was carried out with the aim of seeing the level of satisfaction and usability of the use of Artificial Intelligence in the logistics sector from the stakeholder's point of view. The information obtained in the discussion section shows that use shows a significant impact on indicators of effectiveness, efficiency, and productivity levels.
Development of Adaptive MOOCs to Support Personalized Learning: Mixed Method Analysis P. Priyanto; Ahmad Chafid Alwi; Siti Irene Astuti Dwiningrum; Amrih Setyo Raharjo; Akhsin Nurlayli
Elinvo (Electronics, Informatics, and Vocational Education) Vol 7, No 2 (2022): November 2022
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (636.42 KB) | DOI: 10.21831/elinvo.v7i2.55481

Abstract

This study aims to explain the development of adaptive MOOCs that support personalized learning. This study was designed with a mixed method design of sequential explanatory type at the association level. Quantitative analysis used confirmatory factor analysis (CFA) (n = 110) and was deepened with qualitative analysis of the Miles and Huberman model. Quantitatively measured domains include accessibility, learning curriculum, competence, motivation, satisfaction, efficacy, and self-study. The domain was used as a reference for qualitative data mining through focus group discussions (FGD) involving lecturers and doctoral students (n = 25). The analysis results show that the curriculum domain and one of the motivational indicators should be removed because it did not meet the requirements after bootstrapping. The second running algorithm showed all valid and reliable variables. Some domains that significantly affect MOOC user satisfaction are efficacy, competence, and motivation. R square results showed 37% influenced by motivation, accessibility, efficacy, and self-study, and the rest influenced by other variables. In the qualitative analysis, 19 subcodes were found that were included in the three main codes. In conclusion, there is new information in the accessibility domain that expands quantitative data, including information on MOOCs, marketing traps, regulation, and dropouts. Meanwhile, what strengthens and deepens quantitative data is found in the information on metacognitive and personalized coding that strengthens the domain of efficiency, the domain of competence, which is strengthened by content, mentoring collaboration, and motivation reinforced by coding the user's motivations and goals.
Comparative analysis of Indonesian news validity detection accuracy using machine learning Rachelita Embun Safira; Akhsin Nurlayli
Journal of Engineering and Applied Technology Vol 4, No 1 (2023): (March)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jeatech.v4i1.58791

Abstract

Hoax news prediction is required to anticipate the growth of hoax news in social media. This study aimed to determine the best model for predicting whether the news is a hoax or valid based on the dataset taken from Kaggle.com. This study used several data prediction methods: Support Vector Machine (SVM), Random Forest, Logistic Regression, and Naïve Bayes. After the research processes and data testing, the results showed that the best model for predicting hoax news was SVM, which had the highest accuracy, precision, and recall score of the others.
Co-Authors Ahmad Chafid Alwi Amrih Setyo Raharjo Aulia Zikri Rahman P. Priyanto Rachelita Embun Safira Siti Irene Astuti Dwiningrum Vina Dwiyanti